{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.preprocessing import OneHotEncoder,Binarizer\n",
    "from sklearn.model_selection import train_test_split\n",
    "import numpy as np\n",
    "from sklearn.linear_model import LogisticRegression,LinearRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.metrics import accuracy_score,confusion_matrix,mean_squared_error,recall_score,roc_auc_score,precision_score,f1_score\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "import joblib\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import RocCurveDisplay,ConfusionMatrixDisplay\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import precision_recall_curve\n",
    "from sklearn.feature_extraction.text import CountVectorizer,TfidfVectorizer\n",
    "import jieba\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import ConfusionMatrixDisplay"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [
    {
     "data": {
      "text/plain": "     Sepal Length  Sepal Width  Petal Length  Petal Width  Label\n0             6.4          2.8           5.6          2.2      2\n1             5.0          2.3           3.3          1.0      1\n2             4.9          2.5           4.5          1.7      2\n3             4.9          3.1           1.5          0.1      0\n4             5.7          3.8           1.7          0.3      0\n..            ...          ...           ...          ...    ...\n115           5.5          2.6           4.4          1.2      1\n116           5.7          3.0           4.2          1.2      1\n117           4.4          2.9           1.4          0.2      0\n118           4.8          3.0           1.4          0.1      0\n119           5.5          2.4           3.7          1.0      1\n\n[120 rows x 5 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Sepal Length</th>\n      <th>Sepal Width</th>\n      <th>Petal Length</th>\n      <th>Petal Width</th>\n      <th>Label</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>6.4</td>\n      <td>2.8</td>\n      <td>5.6</td>\n      <td>2.2</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>5.0</td>\n      <td>2.3</td>\n      <td>3.3</td>\n      <td>1.0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>4.9</td>\n      <td>2.5</td>\n      <td>4.5</td>\n      <td>1.7</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4.9</td>\n      <td>3.1</td>\n      <td>1.5</td>\n      <td>0.1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5.7</td>\n      <td>3.8</td>\n      <td>1.7</td>\n      <td>0.3</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>115</th>\n      <td>5.5</td>\n      <td>2.6</td>\n      <td>4.4</td>\n      <td>1.2</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>116</th>\n      <td>5.7</td>\n      <td>3.0</td>\n      <td>4.2</td>\n      <td>1.2</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>117</th>\n      <td>4.4</td>\n      <td>2.9</td>\n      <td>1.4</td>\n      <td>0.2</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>118</th>\n      <td>4.8</td>\n      <td>3.0</td>\n      <td>1.4</td>\n      <td>0.1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>119</th>\n      <td>5.5</td>\n      <td>2.4</td>\n      <td>3.7</td>\n      <td>1.0</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n<p>120 rows × 5 columns</p>\n</div>"
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"C:\\\\Users\\\\Administrator\\\\Desktop\\\\月考练习算法题 (2)\\\\月考练习算法题\\\\大数据-专高六《机器学习》-第5套\\\\专高6月考-05附件\\\\第二题数据\\\\鸢尾花.csv\")\n",
    "df"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [
    {
     "data": {
      "text/plain": "       Sepal Length  Sepal Width  Petal Length  Petal Width       Label\ncount    120.000000   120.000000    120.000000   120.000000  120.000000\nmean       5.845000     3.065000      3.739167     1.196667    1.000000\nstd        0.868578     0.427156      1.822100     0.782039    0.840168\nmin        4.400000     2.000000      1.000000     0.100000    0.000000\n25%        5.075000     2.800000      1.500000     0.300000    0.000000\n50%        5.800000     3.000000      4.400000     1.300000    1.000000\n75%        6.425000     3.300000      5.100000     1.800000    2.000000\nmax        7.900000     4.400000      6.900000     2.500000    2.000000",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Sepal Length</th>\n      <th>Sepal Width</th>\n      <th>Petal Length</th>\n      <th>Petal Width</th>\n      <th>Label</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>120.000000</td>\n      <td>120.000000</td>\n      <td>120.000000</td>\n      <td>120.000000</td>\n      <td>120.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>5.845000</td>\n      <td>3.065000</td>\n      <td>3.739167</td>\n      <td>1.196667</td>\n      <td>1.000000</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>0.868578</td>\n      <td>0.427156</td>\n      <td>1.822100</td>\n      <td>0.782039</td>\n      <td>0.840168</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>4.400000</td>\n      <td>2.000000</td>\n      <td>1.000000</td>\n      <td>0.100000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>5.075000</td>\n      <td>2.800000</td>\n      <td>1.500000</td>\n      <td>0.300000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>5.800000</td>\n      <td>3.000000</td>\n      <td>4.400000</td>\n      <td>1.300000</td>\n      <td>1.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>6.425000</td>\n      <td>3.300000</td>\n      <td>5.100000</td>\n      <td>1.800000</td>\n      <td>2.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>7.900000</td>\n      <td>4.400000</td>\n      <td>6.900000</td>\n      <td>2.500000</td>\n      <td>2.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [
    {
     "data": {
      "text/plain": "     Sepal Length  Sepal Width  Petal Length  Petal Width\n0             6.4          2.8           5.6          2.2\n1             5.0          2.3           3.3          1.0\n2             4.9          2.5           4.5          1.7\n3             4.9          3.1           1.5          0.1\n4             5.7          3.8           1.7          0.3\n..            ...          ...           ...          ...\n115           5.5          2.6           4.4          1.2\n116           5.7          3.0           4.2          1.2\n117           4.4          2.9           1.4          0.2\n118           4.8          3.0           1.4          0.1\n119           5.5          2.4           3.7          1.0\n\n[120 rows x 4 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Sepal Length</th>\n      <th>Sepal Width</th>\n      <th>Petal Length</th>\n      <th>Petal Width</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>6.4</td>\n      <td>2.8</td>\n      <td>5.6</td>\n      <td>2.2</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>5.0</td>\n      <td>2.3</td>\n      <td>3.3</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>4.9</td>\n      <td>2.5</td>\n      <td>4.5</td>\n      <td>1.7</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4.9</td>\n      <td>3.1</td>\n      <td>1.5</td>\n      <td>0.1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5.7</td>\n      <td>3.8</td>\n      <td>1.7</td>\n      <td>0.3</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>115</th>\n      <td>5.5</td>\n      <td>2.6</td>\n      <td>4.4</td>\n      <td>1.2</td>\n    </tr>\n    <tr>\n      <th>116</th>\n      <td>5.7</td>\n      <td>3.0</td>\n      <td>4.2</td>\n      <td>1.2</td>\n    </tr>\n    <tr>\n      <th>117</th>\n      <td>4.4</td>\n      <td>2.9</td>\n      <td>1.4</td>\n      <td>0.2</td>\n    </tr>\n    <tr>\n      <th>118</th>\n      <td>4.8</td>\n      <td>3.0</td>\n      <td>1.4</td>\n      <td>0.1</td>\n    </tr>\n    <tr>\n      <th>119</th>\n      <td>5.5</td>\n      <td>2.4</td>\n      <td>3.7</td>\n      <td>1.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>120 rows × 4 columns</p>\n</div>"
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df.iloc[:,:-1]\n",
    "y = df[\"Label\"]\n",
    "X\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [
    {
     "data": {
      "text/plain": "     Sepal Length  Sepal Width  Petal Length  Petal Width\n30            6.3          2.3           4.4          1.3\n53            5.0          3.2           1.2          0.2\n118           4.8          3.0           1.4          0.1\n9             5.1          3.7           1.5          0.4\n33            6.8          3.0           5.5          2.1\n..            ...          ...           ...          ...\n106           5.3          3.7           1.5          0.2\n14            7.7          3.8           6.7          2.2\n92            5.1          3.5           1.4          0.3\n51            6.6          3.0           4.4          1.4\n102           4.5          2.3           1.3          0.3\n\n[84 rows x 4 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Sepal Length</th>\n      <th>Sepal Width</th>\n      <th>Petal Length</th>\n      <th>Petal Width</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>30</th>\n      <td>6.3</td>\n      <td>2.3</td>\n      <td>4.4</td>\n      <td>1.3</td>\n    </tr>\n    <tr>\n      <th>53</th>\n      <td>5.0</td>\n      <td>3.2</td>\n      <td>1.2</td>\n      <td>0.2</td>\n    </tr>\n    <tr>\n      <th>118</th>\n      <td>4.8</td>\n      <td>3.0</td>\n      <td>1.4</td>\n      <td>0.1</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>5.1</td>\n      <td>3.7</td>\n      <td>1.5</td>\n      <td>0.4</td>\n    </tr>\n    <tr>\n      <th>33</th>\n      <td>6.8</td>\n      <td>3.0</td>\n      <td>5.5</td>\n      <td>2.1</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>106</th>\n      <td>5.3</td>\n      <td>3.7</td>\n      <td>1.5</td>\n      <td>0.2</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>7.7</td>\n      <td>3.8</td>\n      <td>6.7</td>\n      <td>2.2</td>\n    </tr>\n    <tr>\n      <th>92</th>\n      <td>5.1</td>\n      <td>3.5</td>\n      <td>1.4</td>\n      <td>0.3</td>\n    </tr>\n    <tr>\n      <th>51</th>\n      <td>6.6</td>\n      <td>3.0</td>\n      <td>4.4</td>\n      <td>1.4</td>\n    </tr>\n    <tr>\n      <th>102</th>\n      <td>4.5</td>\n      <td>2.3</td>\n      <td>1.3</td>\n      <td>0.3</td>\n    </tr>\n  </tbody>\n</table>\n<p>84 rows × 4 columns</p>\n</div>"
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n",
    "X_train\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "outputs": [],
   "source": [
    "# 线性\n",
    "svm_linear  = SVC(kernel='linear',C=1)\n",
    "svm_linear.fit(X_train,y_train)\n",
    "linear_pred = svm_linear.predict(X_test)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "outputs": [],
   "source": [
    "# 多项式\n",
    "svm_poly  = SVC(kernel='poly',C=1)\n",
    "svm_poly.fit(X_train,y_train)\n",
    "poly_pred = svm_poly.predict(X_test)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "outputs": [],
   "source": [
    "svm_rbf  = SVC(kernel='rbf',C=1)\n",
    "svm_rbf.fit(X_train,y_train)\n",
    "rfb_pred = svm_rbf.predict(X_test)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 2 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test,linear_pred)\n",
    "cm_display = ConfusionMatrixDisplay(cm).plot()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 2 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm1 = confusion_matrix(y_test,poly_pred)\n",
    "cm_display = ConfusionMatrixDisplay(cm1).plot()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 2 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm2 = confusion_matrix(y_test,rfb_pred)\n",
    "cm_display = ConfusionMatrixDisplay(cm2).plot()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X_test.iloc[:, 0],X_test.iloc[:, -1],c=linear_pred)\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "outputs": [
    {
     "data": {
      "text/plain": "     Sepal Length  Sepal Width  Petal Length  Petal Width\n44            4.7          3.2           1.6          0.2\n47            7.7          2.6           6.9          2.3\n4             5.7          3.8           1.7          0.3\n55            6.0          3.0           4.8          1.8\n26            5.8          2.6           4.0          1.2\n64            6.2          2.2           4.5          1.5\n73            7.7          3.0           6.1          2.3\n10            5.2          2.7           3.9          1.4\n40            7.9          3.8           6.4          2.0\n107           5.0          3.6           1.4          0.2\n18            6.4          3.2           5.3          2.3\n62            6.3          2.5           5.0          1.9\n11            6.9          3.1           4.9          1.5\n36            6.5          3.2           5.1          2.0\n89            5.0          3.5           1.3          0.3\n91            6.1          2.8           4.7          1.2\n109           4.8          3.1           1.6          0.2\n0             6.4          2.8           5.6          2.2\n88            6.0          2.7           5.1          1.6\n104           5.0          3.4           1.6          0.4\n65            7.3          2.9           6.3          1.8\n45            6.0          2.2           5.0          1.5\n31            5.1          2.5           3.0          1.1\n70            4.7          3.2           1.3          0.2\n42            6.7          3.0           5.2          2.3\n12            5.8          4.0           1.2          0.2\n15            6.3          3.3           4.7          1.6\n114           5.0          3.5           1.6          0.6\n76            5.5          2.4           3.8          1.1\n97            7.7          2.8           6.7          2.0\n24            7.2          3.0           5.8          1.6\n78            6.0          2.9           4.5          1.5\n22            5.4          3.9           1.3          0.4\n96            4.6          3.2           1.4          0.2\n56            7.4          2.8           6.1          1.9\n110           6.3          2.7           4.9          1.8",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Sepal Length</th>\n      <th>Sepal Width</th>\n      <th>Petal Length</th>\n      <th>Petal Width</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>44</th>\n      <td>4.7</td>\n      <td>3.2</td>\n      <td>1.6</td>\n      <td>0.2</td>\n    </tr>\n    <tr>\n      <th>47</th>\n      <td>7.7</td>\n      <td>2.6</td>\n      <td>6.9</td>\n      <td>2.3</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5.7</td>\n      <td>3.8</td>\n      <td>1.7</td>\n      <td>0.3</td>\n    </tr>\n    <tr>\n      <th>55</th>\n      <td>6.0</td>\n      <td>3.0</td>\n      <td>4.8</td>\n      <td>1.8</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>5.8</td>\n      <td>2.6</td>\n      <td>4.0</td>\n      <td>1.2</td>\n    </tr>\n    <tr>\n      <th>64</th>\n      <td>6.2</td>\n      <td>2.2</td>\n      <td>4.5</td>\n      <td>1.5</td>\n    </tr>\n    <tr>\n      <th>73</th>\n      <td>7.7</td>\n      <td>3.0</td>\n      <td>6.1</td>\n      <td>2.3</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>5.2</td>\n      <td>2.7</td>\n      <td>3.9</td>\n      <td>1.4</td>\n    </tr>\n    <tr>\n      <th>40</th>\n      <td>7.9</td>\n      <td>3.8</td>\n      <td>6.4</td>\n      <td>2.0</td>\n    </tr>\n    <tr>\n      <th>107</th>\n      <td>5.0</td>\n      <td>3.6</td>\n      <td>1.4</td>\n      <td>0.2</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>6.4</td>\n      <td>3.2</td>\n      <td>5.3</td>\n      <td>2.3</td>\n    </tr>\n    <tr>\n      <th>62</th>\n      <td>6.3</td>\n      <td>2.5</td>\n      <td>5.0</td>\n      <td>1.9</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>6.9</td>\n      <td>3.1</td>\n      <td>4.9</td>\n      <td>1.5</td>\n    </tr>\n    <tr>\n      <th>36</th>\n      <td>6.5</td>\n      <td>3.2</td>\n      <td>5.1</td>\n      <td>2.0</td>\n    </tr>\n    <tr>\n      <th>89</th>\n      <td>5.0</td>\n      <td>3.5</td>\n      <td>1.3</td>\n      <td>0.3</td>\n    </tr>\n    <tr>\n      <th>91</th>\n      <td>6.1</td>\n      <td>2.8</td>\n      <td>4.7</td>\n      <td>1.2</td>\n    </tr>\n    <tr>\n      <th>109</th>\n      <td>4.8</td>\n      <td>3.1</td>\n      <td>1.6</td>\n      <td>0.2</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>6.4</td>\n      <td>2.8</td>\n      <td>5.6</td>\n      <td>2.2</td>\n    </tr>\n    <tr>\n      <th>88</th>\n      <td>6.0</td>\n      <td>2.7</td>\n      <td>5.1</td>\n      <td>1.6</td>\n    </tr>\n    <tr>\n      <th>104</th>\n      <td>5.0</td>\n      <td>3.4</td>\n      <td>1.6</td>\n      <td>0.4</td>\n    </tr>\n    <tr>\n      <th>65</th>\n      <td>7.3</td>\n      <td>2.9</td>\n      <td>6.3</td>\n      <td>1.8</td>\n    </tr>\n    <tr>\n      <th>45</th>\n      <td>6.0</td>\n      <td>2.2</td>\n      <td>5.0</td>\n      <td>1.5</td>\n    </tr>\n    <tr>\n      <th>31</th>\n      <td>5.1</td>\n      <td>2.5</td>\n      <td>3.0</td>\n      <td>1.1</td>\n    </tr>\n    <tr>\n      <th>70</th>\n      <td>4.7</td>\n      <td>3.2</td>\n      <td>1.3</td>\n      <td>0.2</td>\n    </tr>\n    <tr>\n      <th>42</th>\n      <td>6.7</td>\n      <td>3.0</td>\n      <td>5.2</td>\n      <td>2.3</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>5.8</td>\n      <td>4.0</td>\n      <td>1.2</td>\n      <td>0.2</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>6.3</td>\n      <td>3.3</td>\n      <td>4.7</td>\n      <td>1.6</td>\n    </tr>\n    <tr>\n      <th>114</th>\n      <td>5.0</td>\n      <td>3.5</td>\n      <td>1.6</td>\n      <td>0.6</td>\n    </tr>\n    <tr>\n      <th>76</th>\n      <td>5.5</td>\n      <td>2.4</td>\n      <td>3.8</td>\n      <td>1.1</td>\n    </tr>\n    <tr>\n      <th>97</th>\n      <td>7.7</td>\n      <td>2.8</td>\n      <td>6.7</td>\n      <td>2.0</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>7.2</td>\n      <td>3.0</td>\n      <td>5.8</td>\n      <td>1.6</td>\n    </tr>\n    <tr>\n      <th>78</th>\n      <td>6.0</td>\n      <td>2.9</td>\n      <td>4.5</td>\n      <td>1.5</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>5.4</td>\n      <td>3.9</td>\n      <td>1.3</td>\n      <td>0.4</td>\n    </tr>\n    <tr>\n      <th>96</th>\n      <td>4.6</td>\n      <td>3.2</td>\n      <td>1.4</td>\n      <td>0.2</td>\n    </tr>\n    <tr>\n      <th>56</th>\n      <td>7.4</td>\n      <td>2.8</td>\n      <td>6.1</td>\n      <td>1.9</td>\n    </tr>\n    <tr>\n      <th>110</th>\n      <td>6.3</td>\n      <td>2.7</td>\n      <td>4.9</td>\n      <td>1.8</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 800x600 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# # todo\n",
    "plt.figure(figsize=(8, 6))\n",
    "colors = ['red', 'green', 'blue']\n",
    "for i in range(3):\n",
    "    # 绘制每个类别的点\n",
    "    plt.scatter(X_train.values[y_train == i, 0], X_train.values[y_train == i, 1],\n",
    "                c=colors[i])\n",
    "# 添加图例\n",
    "plt.legend()\n",
    "# 添加标题和坐标轴标签\n",
    "plt.title('Iris Classification with SVM (RBF Kernel)')\n",
    "plt.xlabel('Sepal length (scaled)')\n",
    "plt.ylabel('Sepal width (scaled)')\n",
    "# 显示图形\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "outputs": [
    {
     "data": {
      "text/plain": "30     1\n53     0\n118    0\n9      0\n33     2\n      ..\n106    0\n14     2\n92     0\n51     1\n102    0\nName: Label, Length: 84, dtype: int64"
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 样本区间图\n",
    "# 鸢尾花分类可视化\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}